Transportation is an area requiring attention for many of the world's cities. In situations where intelligent transportation systems (ITS) are used in an effort to manage traffic, city authorities often need to decide what sensors to use to get traffic data for traffic in the region. Multiple approaches exist, varying in accuracy, coverage and cost to install and maintain. Accordingly, a city or other entity can make an initial decision, but with existing approaches, that decision will need to be continually re-visited over time as traffic patterns and technology changes.
Also, existing approaches include merely selecting one sensor method (for example, global positioning system (GPS)) and ignoring other sensing data. Additionally, challenges arise in existing approaches when traffic is mixed and its movement is chaotic. Accordingly, a need exists for a technique incorporating sensors with high coverage, high-accuracy, low-cost, and maintainability.